Style transfer practices have conjectured that styles are homogeneous, consisting of repeated structural motifs. Two images are similar in style if the features extracted by a trained classifier share the same statistics [1]}, [2]}, such as first- and second-order statistics, which are used due to their computational efficiency. The first- and second-order statistics refer to the mean and variance of the extracted features, which are called style features. Object features refer to pure semantic information without style information. Similar to [3]}, the object features \(f_o\)  can be obtained by cleaning style features as:
\(f_o = \frac{{f_c - {\mu (f_c)}}}{{{\sigma (f_c)}}},\) 
